EDW Manager at a university with 1,001-5,000 employees
Real User
Top 10
Dec 19, 2025
I do not utilize Dell PowerStore or Dremio because I work for a university setting with a very simple infrastructure, where we just use Cognos and IBM DataStage. I do not know if my organization uses AWS as a main cloud provider. We are not on the cloud in a major way and are still on-premises for most of our solutions. In fact, even IBM DataStage, we are using IBM Cloud Pak for Data version, but it is installed on-premises, and we haven't progressed much on how to migrate to the cloud yet. I am not sure if we use AWS as a cloud provider since we do have some SaaS applications that we subscribe to, but I do not know where they are hosted. I just know we have access to the application for the user interface, and the data is pulled out using an API, but we do not know where it is hosted. I do not utilize Cognos ad hoc reporting because I do not develop reports. We only host the Cognos infrastructure for our different user groups, and the report development is completed by them. Our infrastructure team provides the hardware, and our system engineering team provides the installation and application maintenance for Cognos. I think some users are using the interactive dashboards feature, and there are also other tools such as Power BI and Tableau that some users automatically use. However, our IT organization only provides Cognos as an enterprise business intelligence and reporting tool. Other tools are subscribed to separately by different people. I am not the right person to speak on the machine learning capabilities, as my responsibility is to work with different IT teams who maintain systems across the university. I connect to them using IBM DataStage to fetch their data, perform ETL activities, and load the data into an Oracle database. My team maintains the infrastructure for DataStage and Cognos, but actual development is done by other people. I use IBM DataStage, which we call IBM Cloud Pak for Data, as we migrated from InfoSphere DataStage to IBM Cloud Pak for Data, and it is installed on-premises in our data center. IBM Cloud Pak for Data version is more or less a modern OpenShift cluster-based platform. The best features of IBM Cloud Pak for Data include a very modern approach to providing data capabilities under one umbrella, with various services such as artificial intelligence and machine learning capabilities, real-time integration, and data virtualization, though each has separate licenses associated with them. We are currently only using the DataStage license. We have not evaluated data virtualization, but I recognize it as a good capability for exploring and experimenting with data, especially for those unfamiliar with data modeling. However, we are not using it due to cost considerations. The developer productivity for DataStage on IBM Cloud Pak for Data is the same as on the old tool, InfoSphere. It does not change anything because the core capabilities remain consistent. Overall, I would rate Cognos a nine out of ten from a pure infrastructure stability and support perspective because we are comfortable and know what to do, considering the long-term use of Cognos. Overall, I would rate IBM Cloud Pak for Data a nine out of ten in terms of capabilities. It mirrors the traditional InfoSphere version of DataStage with a good ETL tool that covers all features expected from such tools. We did not purchase through a marketplace such as AWS. This is all from a long association with IBM directly through negotiations with our procurement team, as we have been a large IBM customer for many years. I would rate this review a nine out of ten overall.
Data asset management engineer at a tech services company with 1-10 employees
Real User
Top 10
Jan 15, 2025
Cloud Pak is a very, very, very good system. I'm super impressed with it. The learning curve is high, but I gain so much when I finally figure it out. Overall product rating: seven out of ten.
If people are with the existing stuff, I would definitely suggest they go with IBM Cloud Pak for Data. I usually recommend the solution for the financial sector, where I worked for about ten years. I worked with IBM for almost eight years. Unless they want to migrate to a new product completely, I recommend IBM Cloud Pak for Data to explore current business. It is easy to integrate the tool with other solutions. Except for metadata queries, metadata validations, and metadata integrations, I don't see any issues with the solution. I would recommend the solution to other users if it supports their existing infrastructure. Some people don't want to put their data in the cloud because they are concerned about how the data is secured with encryption and decryption. For such cases, we have listed out all the pros and cons of the solution to suggest them to users. Overall, I rate the solution a seven out of ten.
I was not fully part of the core team or implementation. The solution is suitable for data analytics and digital transformation. People considering the solution must decide their use case before exploring the tool’s functionalities or components. Typically, people use an IT-heavy approach, adopting functionalities first and then looking for use cases. I would advise them to do the exact opposite. Whatever we do should be in line with the business purpose and the business vision. We must be mindful of the business model and the organization's maturity to consume the insights. These technologies are targeted towards delivering the right decision-making aids. We should not make it an IT agenda. Overall, I rate the product a nine out of ten.
You need to have sufficient funds and experienced personnel because it is a highly technical solution. It's not something that can be easily implemented without proper knowledge and expertise. For management, I would suggest taking smaller steps and gradually adapting the product within the company. Starting with smaller projects rather than diving into a major implementation. The solution has a lot of potential, so I would rate it a nine out of ten.
Lead Architect at a financial services firm with 10,001+ employees
Real User
Apr 24, 2023
My suggestion for those considering using IBM Cloud Pak for Data is to evaluate it based on their own organization's policies and standards. While the product may be technically sound, its fit within the organization's system is the key factor to consider. In our case, the solution has the potential to be beneficial due to its scalability and use of different NFA's, which align with our organization's IT success. Overall, I rate the product a nine out of ten.
I recommend IBM Cloud Pak for Data to others who want to use it. Additionally, I suggest that those who plan to use the solution should train their staff properly. The performance and integration of the solution are fine. Overall, I rate the solution probably a six or seven out of ten.
I'm an expert on IBM InfoSphere, but now, I'm working on IBM Cloud Pak for Data. Approximately five thousand people use IBM Cloud Pak for Data within the company. My advice to others looking into implementing IBM Cloud Pak for Data is that it's helpful if you want to do AI. IBM has a package, but it would also depend on the number of mappings you have for the transformations. Ninety percent can be easily migrated, and the remaining ten percent tends to be more complicated. Right now, my company has five hundred thousand mappings, so the remaining ten percent is a considerable number, fifty thousand. It depends on the number of mappings you have. If you have a minimal number of mappings, you can find an alternative solution versus IBM Cloud Pak for Data. If you have poor centralization in mappings or a lot of mappings, IBM Cloud Pak for Data is an excellent solution to try. IBM Cloud Pak for Data is a good solution, so I'm rating it nine out of ten. It has more enhanced logging, cataloging, and other features, so as a solution, IBM Cloud Pak for Data is good. My company is a customer of IBM Cloud Pak for Data.
IBM Data & IA Technology Consultant at a tech services company with 10,001+ employees
Real User
May 15, 2022
I rate Cloud Pak for Data eight out of 10. It's an excellent tool that offers us many valuable features, but you have to understand how the solution will affect your operational model and rethink the way you deal with data. You're not only implementing a new tool but also an operational model.
Software Consultancyy at a tech services company with 10,001+ employees
Real User
Mar 30, 2022
IBM Cloud Pak for Data is a very useful tool because it has the entire gamut of tools. Starting from collecting the data from various sources using direct integration or through data virtualization and then organizing it into catalogs and applying their organization's policies or if they have other enforcement policies on top of that. We can build the model from that data because you can refine the data. There is a lot of AI inside this single solution, we could bring a lot of homogeneity into the organization. The data discovery process and the finding of the right set of data for the right problem will become much easier because it's a very good solution. I rate IBM Cloud Pak for Data a seven out of ten. The main problem that happens in machine learning projects is that people come in and then work on making the models, and then the model is deployed in production. However, I find that there is a bias in collecting the model, and the model has to be more advanced. The model which you have built, it's very much dependent on the data from which the model was built. Most of the solutions have features for this in the new version of the model, while they don't provide support for the version of the data or the parameters of the data on which the model was built. When a new person joins the organization, or if the engineer who has worked on it has left the organization, if a new person comes in, and they don't have that reference data on which the model was built then we have to start from scratch. This is an area where many of the solutions which are on the market don't have a solution. This is where IBM should focus more on providing a solution. It is a major area that not only IBM but other vendors, should start working on to provide a solution. I understand Microsoft is working on a solution for this, and they have a new concept that they are introducing which is called Machine Teaching. If there is only a partial resolution or solution for this problem to structure data, they have to focus more on bringing new, innovative solutions.
IBM Cloud Pak® for Data is a fully-integrated data and AI platform that modernizes how businesses collect, organize and analyze data to infuse AI throughout their organizations. Cloud-native by design, the platform unifies market-leading services spanning the entire analytics lifecycle. From data management, DataOps, governance, business analytics and automated AI, IBM Cloud Pak for Data helps eliminate the need for costly, and often competing, point solutions while providing the information...
I do not utilize Dell PowerStore or Dremio because I work for a university setting with a very simple infrastructure, where we just use Cognos and IBM DataStage. I do not know if my organization uses AWS as a main cloud provider. We are not on the cloud in a major way and are still on-premises for most of our solutions. In fact, even IBM DataStage, we are using IBM Cloud Pak for Data version, but it is installed on-premises, and we haven't progressed much on how to migrate to the cloud yet. I am not sure if we use AWS as a cloud provider since we do have some SaaS applications that we subscribe to, but I do not know where they are hosted. I just know we have access to the application for the user interface, and the data is pulled out using an API, but we do not know where it is hosted. I do not utilize Cognos ad hoc reporting because I do not develop reports. We only host the Cognos infrastructure for our different user groups, and the report development is completed by them. Our infrastructure team provides the hardware, and our system engineering team provides the installation and application maintenance for Cognos. I think some users are using the interactive dashboards feature, and there are also other tools such as Power BI and Tableau that some users automatically use. However, our IT organization only provides Cognos as an enterprise business intelligence and reporting tool. Other tools are subscribed to separately by different people. I am not the right person to speak on the machine learning capabilities, as my responsibility is to work with different IT teams who maintain systems across the university. I connect to them using IBM DataStage to fetch their data, perform ETL activities, and load the data into an Oracle database. My team maintains the infrastructure for DataStage and Cognos, but actual development is done by other people. I use IBM DataStage, which we call IBM Cloud Pak for Data, as we migrated from InfoSphere DataStage to IBM Cloud Pak for Data, and it is installed on-premises in our data center. IBM Cloud Pak for Data version is more or less a modern OpenShift cluster-based platform. The best features of IBM Cloud Pak for Data include a very modern approach to providing data capabilities under one umbrella, with various services such as artificial intelligence and machine learning capabilities, real-time integration, and data virtualization, though each has separate licenses associated with them. We are currently only using the DataStage license. We have not evaluated data virtualization, but I recognize it as a good capability for exploring and experimenting with data, especially for those unfamiliar with data modeling. However, we are not using it due to cost considerations. The developer productivity for DataStage on IBM Cloud Pak for Data is the same as on the old tool, InfoSphere. It does not change anything because the core capabilities remain consistent. Overall, I would rate Cognos a nine out of ten from a pure infrastructure stability and support perspective because we are comfortable and know what to do, considering the long-term use of Cognos. Overall, I would rate IBM Cloud Pak for Data a nine out of ten in terms of capabilities. It mirrors the traditional InfoSphere version of DataStage with a good ETL tool that covers all features expected from such tools. We did not purchase through a marketplace such as AWS. This is all from a long association with IBM directly through negotiations with our procurement team, as we have been a large IBM customer for many years. I would rate this review a nine out of ten overall.
Cloud Pak is a very, very, very good system. I'm super impressed with it. The learning curve is high, but I gain so much when I finally figure it out. Overall product rating: seven out of ten.
If people are with the existing stuff, I would definitely suggest they go with IBM Cloud Pak for Data. I usually recommend the solution for the financial sector, where I worked for about ten years. I worked with IBM for almost eight years. Unless they want to migrate to a new product completely, I recommend IBM Cloud Pak for Data to explore current business. It is easy to integrate the tool with other solutions. Except for metadata queries, metadata validations, and metadata integrations, I don't see any issues with the solution. I would recommend the solution to other users if it supports their existing infrastructure. Some people don't want to put their data in the cloud because they are concerned about how the data is secured with encryption and decryption. For such cases, we have listed out all the pros and cons of the solution to suggest them to users. Overall, I rate the solution a seven out of ten.
I was not fully part of the core team or implementation. The solution is suitable for data analytics and digital transformation. People considering the solution must decide their use case before exploring the tool’s functionalities or components. Typically, people use an IT-heavy approach, adopting functionalities first and then looking for use cases. I would advise them to do the exact opposite. Whatever we do should be in line with the business purpose and the business vision. We must be mindful of the business model and the organization's maturity to consume the insights. These technologies are targeted towards delivering the right decision-making aids. We should not make it an IT agenda. Overall, I rate the product a nine out of ten.
You need to have sufficient funds and experienced personnel because it is a highly technical solution. It's not something that can be easily implemented without proper knowledge and expertise. For management, I would suggest taking smaller steps and gradually adapting the product within the company. Starting with smaller projects rather than diving into a major implementation. The solution has a lot of potential, so I would rate it a nine out of ten.
My suggestion for those considering using IBM Cloud Pak for Data is to evaluate it based on their own organization's policies and standards. While the product may be technically sound, its fit within the organization's system is the key factor to consider. In our case, the solution has the potential to be beneficial due to its scalability and use of different NFA's, which align with our organization's IT success. Overall, I rate the product a nine out of ten.
I recommend IBM Cloud Pak for Data to others who want to use it. Additionally, I suggest that those who plan to use the solution should train their staff properly. The performance and integration of the solution are fine. Overall, I rate the solution probably a six or seven out of ten.
I would 100% recommend Cloud Pak and would rate it eight out of ten.
I rate the solution a nine out of ten and recommend it to others.
I'm an expert on IBM InfoSphere, but now, I'm working on IBM Cloud Pak for Data. Approximately five thousand people use IBM Cloud Pak for Data within the company. My advice to others looking into implementing IBM Cloud Pak for Data is that it's helpful if you want to do AI. IBM has a package, but it would also depend on the number of mappings you have for the transformations. Ninety percent can be easily migrated, and the remaining ten percent tends to be more complicated. Right now, my company has five hundred thousand mappings, so the remaining ten percent is a considerable number, fifty thousand. It depends on the number of mappings you have. If you have a minimal number of mappings, you can find an alternative solution versus IBM Cloud Pak for Data. If you have poor centralization in mappings or a lot of mappings, IBM Cloud Pak for Data is an excellent solution to try. IBM Cloud Pak for Data is a good solution, so I'm rating it nine out of ten. It has more enhanced logging, cataloging, and other features, so as a solution, IBM Cloud Pak for Data is good. My company is a customer of IBM Cloud Pak for Data.
I rate Cloud Pak for Data eight out of 10. It's an excellent tool that offers us many valuable features, but you have to understand how the solution will affect your operational model and rethink the way you deal with data. You're not only implementing a new tool but also an operational model.
IBM Cloud Pak for Data is a very useful tool because it has the entire gamut of tools. Starting from collecting the data from various sources using direct integration or through data virtualization and then organizing it into catalogs and applying their organization's policies or if they have other enforcement policies on top of that. We can build the model from that data because you can refine the data. There is a lot of AI inside this single solution, we could bring a lot of homogeneity into the organization. The data discovery process and the finding of the right set of data for the right problem will become much easier because it's a very good solution. I rate IBM Cloud Pak for Data a seven out of ten. The main problem that happens in machine learning projects is that people come in and then work on making the models, and then the model is deployed in production. However, I find that there is a bias in collecting the model, and the model has to be more advanced. The model which you have built, it's very much dependent on the data from which the model was built. Most of the solutions have features for this in the new version of the model, while they don't provide support for the version of the data or the parameters of the data on which the model was built. When a new person joins the organization, or if the engineer who has worked on it has left the organization, if a new person comes in, and they don't have that reference data on which the model was built then we have to start from scratch. This is an area where many of the solutions which are on the market don't have a solution. This is where IBM should focus more on providing a solution. It is a major area that not only IBM but other vendors, should start working on to provide a solution. I understand Microsoft is working on a solution for this, and they have a new concept that they are introducing which is called Machine Teaching. If there is only a partial resolution or solution for this problem to structure data, they have to focus more on bringing new, innovative solutions.
I would rate this solution a five out of ten.